scLANE Simulation Study - Trajectory DE Method Comparison

UF Dept. of Biostatistics - Bacher Group

Libraries

library(dplyr)
library(ggplot2)
library(targets)

Helper Functions

source("R/functions_analysis.R")

Data

tar_load(metric_table_master)

Analysis

Reference Dataset

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = ROC_AUC, y = SIM_REFERENCE, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  facet_wrap(~MODEL_TYPE) + 
  ggridges::geom_density_ridges(alpha = 0.6, scale = 0.95, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent()) + 
  labs(y = "Reference Dataset", x = "ROC-AUC") + 
  theme_analysis() + 
  theme(legend.position = "none")

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = F_MEASURE, y = SIM_REFERENCE, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  facet_wrap(~MODEL_TYPE) + 
  ggridges::geom_density_ridges(alpha = 0.6, scale = 0.95, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(accuracy = 0.1), 
                     limits = c(NA, 1)) + 
  labs(y = "Reference Dataset", x = "F-measure") + 
  theme_analysis() + 
  theme(legend.position = "none")

Runtime

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS), 
         RUNTIME_MINS = RUNTIME_HOURS * 60) %>% 
  ggplot(aes(x = N_CELLS, y = RUNTIME_MINS, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_number(accuracy = 1, suffix = "min")) + 
  labs(x = "Cells", y = "Runtime") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(RUNTIME_MINS = RUNTIME_HOURS * 60, 
         GENES_PER_MIN = N_GENES / RUNTIME_MINS) %>% 
  ggplot(aes(x = N_CELLS, y = GENES_PER_MIN, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_smooth(method = "lm", alpha = 0.25) + 
  scale_y_continuous(labels = scales::label_number(accuracy = 1)) + 
  labs(x = "Cells", y = "Genes per Minute") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 0, linewidth = 2)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(RUNTIME_MINS = RUNTIME_HOURS * 60, 
         GENES_PER_MIN = N_GENES / RUNTIME_MINS) %>% 
  ggplot(aes(x = GENES_PER_MIN, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(accuracy = 1)) + 
  labs(x = "Genes per Minute", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Memory Usage

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS), 
         MEM_USED_GB = MEM_USED / 1000) %>% 
  ggplot(aes(x = N_CELLS, y = MEM_USED_GB, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_number(suffix = "gb")) + 
  labs(x = "Cells", y = "Memory Usage") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(MEM_USED_GB = MEM_USED / 1000) %>% 
  ggplot(aes(x = MEM_USED_GB, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(suffix = "gb")) + 
  labs(x = "Memory Usage", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Predictive Performance

F-measure

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = F_MEASURE, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_number(accuracy = 0.1)) + 
  labs(x = "Cells", y = "F-measure") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = F_MEASURE, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(accuracy = 0.1)) + 
  labs(x = "F-measure", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Balanced Accuracy

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = BAL_ACCURACY, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "Balanced Accuracy") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = BAL_ACCURACY, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Balanced Accuracy", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Recall

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = RECALL, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "Recall") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = RECALL, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Recall", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Accuracy

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = ACCURACY)) + 
  geom_boxplot(aes(color = MODEL_TYPE)) + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "Accuracy") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = ACCURACY, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Accuracy", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

ROC-AUC

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = ROC_AUC, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "ROC-AUC") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = ROC_AUC, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "ROC-AUC", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM")) %>% 
  pull(ROC_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = 1 - specificity, y = sensitivity, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 0, y = 1, yend = 1, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + 
  labs(x = "1 - Specificity", 
      y = "Sensitivity", 
      color = "Cells", 
      title = "scLANE - GLM") + 
  theme_analysis() +  
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("tradeSeq")) %>% 
  pull(ROC_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = 1 - specificity, y = sensitivity, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 0, y = 1, yend = 1, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + 
  labs(x = "1 - Specificity", 
      y = "Sensitivity", 
      color = "Cells", 
      title = "tradeSeq") + 
  theme_analysis() + 
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

PR-AUC

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = AUC_PR, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "PR-AUC") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = AUC_PR, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "PR-AUC", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM")) %>% 
  pull(PR_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = recall, y = precision, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 1, y = 1, yend = 0, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_y_continuous(limits = c(0, 1), labels = scales::percent_format(accuracy = 1)) +
  labs(x = "Recall", 
       y = "Precision", 
       color = "Cells", 
       title = "scLANE - GLM") + 
  theme_analysis() + 
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("tradeSeq")) %>% 
  pull(PR_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = recall, y = precision, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 1, y = 1, yend = 0, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_y_continuous(limits = c(0, 1), labels = scales::percent_format(accuracy = 1)) +
  labs(x = "Recall", 
       y = "Precision", 
       color = "Cells", 
       title = "tradeSeq") + 
  theme_analysis() + 
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

Session Info

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.3 (2023-03-15)
 os       Ubuntu 22.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2023-08-22
 pandoc   2.9.2.1 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version    date (UTC) lib source
 backports              1.4.1      2021-12-13 [2] CRAN (R 4.2.0)
 base64url              1.4        2018-05-14 [2] CRAN (R 4.2.0)
 bigassertr             0.1.6      2023-01-10 [2] CRAN (R 4.2.2)
 bigparallelr           0.3.2      2021-10-02 [2] CRAN (R 4.2.0)
 bigstatsr              1.5.12     2022-10-14 [2] CRAN (R 4.2.2)
 Biobase              * 2.58.0     2022-11-01 [2] Bioconductor
 BiocGenerics         * 0.44.0     2023-02-01 [2] bioc_git2r (@d7cd9c1)
 BiocParallel           1.32.6     2023-03-17 [2] Bioconductor
 bitops                 1.0-7      2021-04-24 [2] CRAN (R 4.2.0)
 boot                   1.3-28.1   2022-11-22 [2] CRAN (R 4.2.2)
 broom                * 1.0.5      2023-06-09 [2] CRAN (R 4.2.3)
 broom.mixed            0.2.9.4    2022-04-17 [2] CRAN (R 4.2.0)
 bslib                  0.5.0      2023-06-09 [2] CRAN (R 4.2.3)
 cachem                 1.0.8      2023-05-01 [2] CRAN (R 4.2.3)
 callr                  3.7.3      2022-11-02 [2] CRAN (R 4.2.2)
 cellranger             1.1.0      2016-07-27 [2] CRAN (R 4.2.0)
 class                  7.3-22     2023-05-03 [4] CRAN (R 4.2.3)
 cli                    3.6.1      2023-03-23 [2] CRAN (R 4.2.3)
 coda                   0.19-4     2020-09-30 [2] CRAN (R 4.2.0)
 codetools              0.2-19     2023-02-01 [4] CRAN (R 4.2.2)
 colorspace             2.1-0      2023-01-23 [2] CRAN (R 4.2.2)
 cowplot                1.1.1      2020-12-30 [2] CRAN (R 4.2.0)
 crayon                 1.5.2      2022-09-29 [2] CRAN (R 4.2.1)
 data.table             1.14.8     2023-02-17 [2] CRAN (R 4.2.3)
 DBI                    1.1.3      2022-06-18 [2] CRAN (R 4.2.0)
 dbplyr                 2.3.3      2023-07-07 [2] CRAN (R 4.2.3)
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 doParallel             1.0.17     2022-02-07 [2] CRAN (R 4.2.0)
 dplyr                * 1.1.2      2023-04-20 [1] CRAN (R 4.2.3)
 edgeR                  3.40.2     2023-02-01 [2] bioc_git2r (@ddb1cb9)
 emmeans                1.8.7      2023-06-23 [2] CRAN (R 4.2.3)
 estimability           1.4.1      2022-08-05 [2] CRAN (R 4.2.0)
 evaluate               0.21       2023-05-05 [2] CRAN (R 4.2.3)
 fansi                  1.0.4      2023-01-22 [2] CRAN (R 4.2.2)
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 fastmap                1.1.1      2023-02-24 [2] CRAN (R 4.2.3)
 flock                  0.7        2016-11-12 [2] CRAN (R 4.2.0)
 forcats              * 1.0.0      2023-01-29 [2] CRAN (R 4.2.2)
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 fs                     1.6.2      2023-04-25 [2] CRAN (R 4.2.3)
 furrr                  0.3.1      2022-08-15 [2] CRAN (R 4.2.0)
 future               * 1.33.0     2023-07-01 [1] CRAN (R 4.2.3)
 future.apply           1.11.0     2023-05-21 [2] CRAN (R 4.2.3)
 future.callr         * 0.8.1      2022-12-14 [1] CRAN (R 4.2.3)
 gamlss                 5.4-12     2023-01-27 [2] CRAN (R 4.2.2)
 gamlss.data            6.0-2      2021-11-07 [2] CRAN (R 4.2.0)
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 gargle                 1.5.1      2023-06-19 [2] CRAN (R 4.2.3)
 geeM                   0.10.1     2018-06-18 [2] CRAN (R 4.2.0)
 generics               0.1.3      2022-07-05 [1] CRAN (R 4.2.0)
 GenomeInfoDb         * 1.34.9     2023-02-02 [2] Bioconductor
 GenomeInfoDbData       1.2.9      2023-01-19 [2] Bioconductor
 GenomicRanges        * 1.50.2     2022-12-16 [2] Bioconductor
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 ggridges               0.5.4      2022-09-26 [2] CRAN (R 4.2.1)
 glm2                 * 1.2.1      2018-08-11 [2] CRAN (R 4.2.0)
 glmmTMB                1.1.7      2023-04-05 [1] CRAN (R 4.2.3)
 globals                0.16.2     2022-11-21 [2] CRAN (R 4.2.2)
 glue                   1.6.2      2022-02-24 [2] CRAN (R 4.2.0)
 googledrive            2.1.1      2023-06-11 [2] CRAN (R 4.2.3)
 googlesheets4          1.1.1      2023-06-11 [2] CRAN (R 4.2.3)
 gower                  1.0.1      2022-12-22 [2] CRAN (R 4.2.2)
 GPfit                  1.0-8      2019-02-08 [2] CRAN (R 4.2.0)
 gridExtra              2.3        2017-09-09 [2] CRAN (R 4.2.0)
 gtable                 0.3.3      2023-03-21 [2] CRAN (R 4.2.3)
 hardhat                1.3.0      2023-03-30 [2] CRAN (R 4.2.3)
 haven                  2.5.3      2023-06-30 [2] CRAN (R 4.2.3)
 highr                  0.10       2022-12-22 [2] CRAN (R 4.2.2)
 hms                    1.1.3      2023-03-21 [2] CRAN (R 4.2.3)
 htmltools              0.5.4      2022-12-07 [1] CRAN (R 4.2.2)
 httr                   1.4.6      2023-05-08 [2] CRAN (R 4.2.3)
 igraph                 1.4.1      2023-02-24 [1] CRAN (R 4.2.2)
 infer                * 1.0.4      2022-12-02 [2] CRAN (R 4.2.2)
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 IRanges              * 2.32.0     2022-11-01 [2] Bioconductor
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 jquerylib              0.1.4      2021-04-26 [2] CRAN (R 4.2.0)
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 knitr                  1.43       2023-05-25 [2] CRAN (R 4.2.3)
 labeling               0.4.2      2020-10-20 [2] CRAN (R 4.2.0)
 lattice                0.21-8     2023-04-05 [4] CRAN (R 4.2.3)
 lava                   1.7.2.1    2023-02-27 [2] CRAN (R 4.2.3)
 lhs                    1.1.6      2022-12-17 [2] CRAN (R 4.2.2)
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 limma                  3.54.2     2023-02-28 [2] Bioconductor
 listenv                0.9.0      2022-12-16 [2] CRAN (R 4.2.2)
 lme4                   1.1-34     2023-07-04 [2] CRAN (R 4.2.3)
 locfit                 1.5-9.8    2023-06-11 [2] CRAN (R 4.2.3)
 lubridate              1.9.2      2023-02-10 [2] CRAN (R 4.2.3)
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 minqa                  1.2.5      2022-10-19 [2] CRAN (R 4.2.2)
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 munsell                0.5.0      2018-06-12 [2] CRAN (R 4.2.0)
 mvtnorm                1.2-2      2023-06-08 [2] CRAN (R 4.2.3)
 nanonext               0.9.1      2023-07-13 [1] CRAN (R 4.2.3)
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 numDeriv               2016.8-1.1 2019-06-06 [2] CRAN (R 4.2.0)
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 processx               3.8.2      2023-06-30 [2] CRAN (R 4.2.3)
 prodlim                2023.03.31 2023-04-02 [2] CRAN (R 4.2.3)
 ps                     1.7.5      2023-04-18 [2] CRAN (R 4.2.3)
 purrr                * 1.0.1      2023-01-10 [1] CRAN (R 4.2.3)
 qs                   * 0.25.4     2022-08-09 [1] CRAN (R 4.2.0)
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 RColorBrewer           1.1-3      2022-04-03 [2] CRAN (R 4.2.0)
 Rcpp                   1.0.11     2023-07-06 [2] CRAN (R 4.2.3)
 RcppEigen              0.3.3.9.3  2022-11-05 [2] CRAN (R 4.2.2)
 RcppParallel           5.1.7      2023-02-27 [2] CRAN (R 4.2.3)
 RCurl                  1.98-1.12  2023-03-27 [2] CRAN (R 4.2.3)
 readr                * 2.1.4      2023-02-10 [2] CRAN (R 4.2.3)
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 stringr              * 1.5.0      2022-12-02 [2] CRAN (R 4.2.2)
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 tibble               * 3.2.1      2023-03-20 [2] CRAN (R 4.2.3)
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 tradeSeq             * 1.10.0     2022-04-26 [1] Bioconductor
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 xml2                   1.3.5      2023-07-06 [2] CRAN (R 4.2.3)
 xtable                 1.8-4      2019-04-21 [2] CRAN (R 4.2.0)
 XVector                0.38.0     2022-11-01 [2] Bioconductor
 yaml                   2.3.7      2023-01-23 [2] CRAN (R 4.2.2)
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 zlibbioc               1.44.0     2022-11-01 [2] Bioconductor
 zoo                    1.8-12     2023-04-13 [2] CRAN (R 4.2.3)

 [1] /home/j.leary/r_packages_default
 [2] /usr/local/lib/R/site-library
 [3] /usr/lib/R/site-library
 [4] /usr/lib/R/library

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